Cause-specific quantile residual life regression

2015 ◽  
Vol 26 (4) ◽  
pp. 1912-1924 ◽  
Author(s):  
Jeong Youn Lim ◽  
Jong-Hyeon Jeong
Keyword(s):  
Residual Life ◽  
Fixed Time ◽  
Estimating Equation ◽  
Simulation Studies ◽  
Finite Sample ◽  
Test Statistic ◽  
Life Distribution ◽  
Finite Sample Properties ◽  
Quantile Residual Life ◽  
Log Linear

We propose a cause-specific quantile residual life regression where the cause-specific quantile residual life, defined as the inverse of the cumulative incidence function of the residual life distribution of a specific type of events of interest conditional on a fixed time point, is log-linear in observable covariates. The proposed test statistic for the effects of prognostic factors does not involve estimation of the improper probability density function of the cause-specific residual life distribution under competing risks. The asymptotic distribution of the test statistic is derived. Simulation studies are performed to assess the finite sample properties of the proposed estimating equation and the test statistic. The proposed method is illustrated with a real dataset from a clinical trial on breast cancer.

A Test for Functional Form Against Nonparametric Alternatives

1992 ◽  
Vol 8 (4) ◽  
pp. 452-475 ◽  
Author(s):  
Jeffrey M. Wooldridge
Keyword(s):  
Alternative Model ◽  
Regression Models ◽  
Functional Form ◽  
Parametric Model ◽  
Finite Sample ◽  
Test Statistic ◽  
Sieve Estimation ◽  
Finite Sample Properties ◽  
Standard Normal ◽  
Nonparametric Alternatives

A test for neglected nonlinearities in regression models is proposed. The test is of the Davidson-MacKinnon type against an increasingly rich set of non-nested alternatives, and is based on sieve estimation of the alternative model. For the case of a linear parametric model, the test statistic is shown to be asymptotically standard normal under the null, while rejecting with probability going to one if the linear model is misspecified. A small simulation study suggests that the test has adequate finite sample properties, but one must guard against over fitting the nonparametric alternative.

scholarly journals Rank Estimation for Mean Residual Life Transformation Model

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Xiaoping Chen
Keyword(s):  
Residual Life ◽  
Rank Correlation ◽  
Transformation Model ◽  
Mean Residual Life ◽  
Finite Sample ◽  
Data Set ◽  
Finite Sample Properties ◽  
Life Model ◽  
The Mean ◽  
Life Transformation

This paper proposes a new and important class of mean residual life regression model, which is called the mean residual life transformation model.  The link function is assumed to be unknown and increasing in its second argument, but it is permitted to be not differentiable. The mean residual life transformation model encompasses the proportional mean residual life model, the additive mean residual life model, and so on. Under maximum rank correlation estimation, we present the estimation procedures, whose asymptotic and finite sample properties are established. The consistent variance can be estimated by a resampling method via perturbing the U -statistics objective function repeatedly which avoids the usual sandwich choice. Monte Carlo simulations reveal good finite sample performance and the estimators are illustrated with the Oscar data set.

Topp–Leone Linear Exponential Distribution

2018 ◽  
Vol 33 (1) ◽  
pp. 31-43
Author(s):  
Bol A. M. Atem ◽  
Suleman Nasiru ◽  
Kwara Nantomah
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Exponential Distribution ◽  
Likelihood Estimation ◽  
Real Data ◽  
Simulation Studies ◽  
Finite Sample ◽  
Data Set ◽  
Finite Sample Properties ◽  
Linear Exponential Distribution

Abstract This article studies the properties of the Topp–Leone linear exponential distribution. The parameters of the new model are estimated using maximum likelihood estimation, and simulation studies are performed to examine the finite sample properties of the parameters. An application of the model is demonstrated using a real data set. Finally, a bivariate extension of the model is proposed.

scholarly journals Fixed and Long Time Span Jump Tests: New Monte Carlo and Empirical Evidence

Econometrics ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 13 ◽  
Author(s):  
Mingmian Cheng ◽  
Norman Swanson
Keyword(s):  
Monte Carlo ◽  
Fixed Time ◽  
Time Span ◽  
Finite Sample ◽  
Long Span ◽  
Finite Sample Properties ◽  
Jump Intensity ◽  
Data Generating Process ◽  
Long Time ◽  
Robust Tests

Numerous tests designed to detect realized jumps over a fixed time span have been proposed and extensively studied in the financial econometrics literature. These tests differ from “long time span tests” that detect jumps by examining the magnitude of the jump intensity parameter in the data generating process, and which are consistent. In this paper, long span jump tests are compared and contrasted with a variety of fixed span jump tests in a series of Monte Carlo experiments. It is found that both the long time span tests of Corradi et al. (2018) and the fixed span tests of Aït-Sahalia and Jacod (2009) exhibit reasonably good finite sample properties, for time spans both short and long. Various other tests suffer from finite sample distortions, both under sequential testing and under long time spans. The latter finding is new, and confirms the “pitfall” discussed in Huang and Tauchen (2005), of using asymptotic approximations associated with finite time span tests in order to study long time spans of data. An empirical analysis is carried out to investigate the implications of these findings, and “time-span robust” tests indicate that the prevalence of jumps is not as universal as might be expected.

scholarly journals SPECIFICATION TESTING IN NONPARAMETRIC INSTRUMENTAL QUANTILE REGRESSION

2020 ◽  
Vol 36 (4) ◽  
pp. 583-625 ◽  
Author(s):  
Christoph Breunig
Keyword(s):  
Quantile Regression ◽  
Regression Model ◽  
Monte Carlo Study ◽  
Test Statistics ◽  
Finite Sample ◽  
Test Statistic ◽  
Specification Testing ◽  
Quantile Regression Model ◽  
Endogenous Regressors ◽  
Finite Sample Properties

There are many environments in econometrics which require nonseparable modeling of a structural disturbance. In a nonseparable model with endogenous regressors, key conditions are validity of instrumental variables and monotonicity of the model in a scalar unobservable variable. Under these conditions the nonseparable model is equivalent to an instrumental quantile regression model. A failure of the key conditions, however, makes instrumental quantile regression potentially inconsistent. This article develops a methodology for testing the hypothesis whether the instrumental quantile regression model is correctly specified. Our test statistic is asymptotically normally distributed under correct specification and consistent against any alternative model. In addition, test statistics to justify the model simplification are established. Finite sample properties are examined in a Monte Carlo study and an empirical illustration is provided.

An intuitive skewness-based symmetry test applicable to stationary time series data

2019 ◽  
Vol 23 (5) ◽  
Author(s):  
Luke Hartigan
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Stationary Time Series ◽  
Series Data ◽  
Finite Sample ◽  
Test Statistic ◽  
Stationary Time ◽  
Symmetry Test ◽  
Finite Sample Properties ◽  
The Difference

Abstract I propose a simple skewness-based test of symmetry suitable for a stationary time series. The test is based on the difference between the squared deviation of a process above its median with that below it. The test has many attractive features: it is applicable to weakly dependent processes, it has a familiar form, it can be implemented using regression, and it has a standard Gaussian limiting distribution under the null hypothesis of symmetry. The finite sample properties of the test statistic are examined via Monte Carlo simulation and suggest that it has better size-adjusted power compared to competing tests in the literature when examining moderately persistence processes. I apply the test to a range of US economic and financial data and find stronger support for asymmetry in financial series compared to economic series.

Asymptotic Properties for Estimators of Hazards Model

2012 ◽  
Vol 461 ◽  
pp. 48-52
Author(s):  
Huan Bin Liu ◽  
Ying Ye
Keyword(s):  
Recurrent Events ◽  
Asymptotic Properties ◽  
Estimating Equation ◽  
Parameter Estimates ◽  
Equation Approach ◽  
Finite Sample ◽  
Time Data ◽  
Hazards Model ◽  
Regression Parameters ◽  
Finite Sample Properties

In this paper, the additive-multiplicative hazards model for gap time data of recurrent events is investigated, and the estimating equation approach is presented for inference about regression parameters. Both asymptotic and finite sample properties of the proposed parameter estimates are established

scholarly journals Basis Expansions for Functional Snippets

Biometrika ◽  
2020 ◽  
Author(s):  
Zhenhua Lin ◽  
Jane-Ling Wang ◽  
Qixian Zhong
Keyword(s):  
Data Analysis ◽  
Convergence Rate ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Covariance Function ◽  
Covariance Estimation ◽  
Simulation Studies ◽  
Finite Sample ◽  
Covariance Functions ◽  
The Mean

Summary Estimation of mean and covariance functions is fundamental for functional data analysis. While this topic has been studied extensively in the literature, a key assumption is that there are enough data in the domain of interest to estimate both the mean and covariance functions. In this paper, we investigate mean and covariance estimation for functional snippets in which observations from a subject are available only in an interval of length strictly (and often much) shorter than the length of the whole interval of interest. For such a sampling plan, no data is available for direct estimation of the off-diagonal region of the covariance function. We tackle this challenge via a basis representation of the covariance function. The proposed estimator enjoys a convergence rate that is adaptive to the smoothness of the underlying covariance function, and has superior finite-sample performance in simulation studies.

scholarly journals REMAXINT: a two-mode clustering-based method for statistical inference on two-way interaction

2021 ◽  
Author(s):  
Zaheer Ahmed ◽  
Alberto Cassese ◽  
Gerard van Breukelen ◽  
Jan Schepers
Keyword(s):  
Null Hypothesis ◽  
Type I Error ◽  
Type I ◽  
The Novel ◽  
Simulation Studies ◽  
Test Statistic ◽  
Clustering Model ◽  
Novel Method ◽  
Main Effects ◽  
Empirical Case Studies

AbstractWe present a novel method, REMAXINT, that captures the gist of two-way interaction in row by column (i.e., two-mode) data, with one observation per cell. REMAXINT is a probabilistic two-mode clustering model that yields two-mode partitions with maximal interaction between row and column clusters. For estimation of the parameters of REMAXINT, we maximize a conditional classification likelihood in which the random row (or column) main effects are conditioned out. For testing the null hypothesis of no interaction between row and column clusters, we propose a $$max-F$$ m a x - F test statistic and discuss its properties. We develop a Monte Carlo approach to obtain its sampling distribution under the null hypothesis. We evaluate the performance of the method through simulation studies. Specifically, for selected values of data size and (true) numbers of clusters, we obtain critical values of the $$max-F$$ m a x - F statistic, determine empirical Type I error rate of the proposed inferential procedure and study its power to reject the null hypothesis. Next, we show that the novel method is useful in a variety of applications by presenting two empirical case studies and end with some concluding remarks.

scholarly journals QUANTILE DOUBLE AUTOREGRESSION

2021 ◽  
pp. 1-47
Author(s):  
Qianqian Zhu ◽  
Guodong Li
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Simulation Studies ◽  
Finite Sample ◽  
Conditional Quantile ◽  
New Model ◽  
Conditional Quantiles ◽  
Financial Time ◽  
Portmanteau Tests ◽  
Strict Stationarity

Many financial time series have varying structures at different quantile levels, and also exhibit the phenomenon of conditional heteroskedasticity at the same time. However, there is presently no time series model that accommodates both of these features. This paper fills the gap by proposing a novel conditional heteroskedastic model called “quantile double autoregression”. The strict stationarity of the new model is derived, and self-weighted conditional quantile estimation is suggested. Two promising properties of the original double autoregressive model are shown to be preserved. Based on the quantile autocorrelation function and self-weighting concept, three portmanteau tests are constructed to check the adequacy of the fitted conditional quantiles. The finite sample performance of the proposed inferential tools is examined by simulation studies, and the need for use of the new model is further demonstrated by analyzing the S&P500 Index.

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